This report contains a descriptive analysis of fisheries in Trujillo, based on the sampled, time-series database of fish landings collected by CORAL and its partners.

Date Preparation

# ==== Data preparation ====

# Preparing columns
dat <- dat |> 
  # Using the cleaned common names as default
  mutate(nc_og = nombre_comun) |> 
  mutate(nombre_comun = str_to_title(nombre_comun_cln)) |> 
  # Adding "sp" to columns where the genus is present but no species is present
  mutate(species = if_else((is.na(species) & !is.na(genus)), 'sp', species)) |> 
  tidyr::unite(nombre_cientifico, genus, species, sep=' ', remove=F, na.rm=T) |> 
  # Factorizing relevant columns
  mutate(comunidad = as.factor(comunidad)) |> 
  mutate(zona_pesca = as.factor(zona_pesca)) |> 
  # Getting year, month, and year-month columns
  mutate(year = year(fecha), month=month(fecha), .after='fecha') |> 
  mutate(ym = paste(year, str_pad(month, 2, 'left', '0'), sep='-'), .after=month) |> 
  mutate(month = month.abb[month]) |> 
  # Factorizing year and month column
  mutate(year = as.factor(year)) |> 
  mutate(month = factor(month, levels=month.abb)) |> 
  # Converting weight to kg
  mutate(peso = peso/1000)

# Removing rows where no date is given
dat <- dat |> filter(!is.na(fecha))

# Removing outliers (detected based on IQR ranges specific to each
# genus/family) - based on weight
dat <- dat |> 
  # A helper grouping variable that uses the family name if the scientific name
  # is not available
  mutate(taxa = if_else(is.na(nombre_cientifico), family, nombre_cientifico)) |> 
  group_by(taxa) |> 
  # Naming outliers for each taxa group
  mutate(isoutlier = ifelse(all(is.na(peso)), 'No', anomalize::iqr(peso))) |>
  ungroup()

# Separating those that are outliers for manual inspection - they don't look
# unreasonable, so not removing anything
outliers <- dat |> filter(isoutlier == "Yes")

# Cleaing outliers
dat <- dat |> 
  # Removing unreasonable weight rows
  filter(peso < 40) |> 
  # filter(isoutlier == "No") |> 
  # Removing outlier related fields
  select(-isoutlier, -taxa)

General characteristics

1. Most-caught fish species

10 most-caught species by number and weight

Total captured weight for the 10-most caught species, split by year

2. Most-used gears

3. Catch-seasonality

Overall Catch-seasonality

Catch-seasonality split by fishing gear

4. Shannon-Diversity of caught species and gear by community

## [1] "Diversided de especias capturadas por comunidad:"
## Capiro Jerico      Castilla     Cristales     Rio Negro    San Martin 
##      2.532314      2.404296      2.395181      2.023227      2.263530 
##         Silin 
##      1.064363
## [1] "Diversidad de especias capturadas por tipo de arte:"
##         Chinchorro             Cuerda Cuerda y trasmallo               Nasa 
##          0.8723983          2.3582201          0.9649629          2.0524470 
##          Trasmallo 
##          2.5229556
## [1] "Diversidad de tipos de artes por comunidad:"
## Capiro Jerico      Castilla     Cristales     Rio Negro    San Martin 
##     0.2605994     0.4509718     0.5801219     0.0000000     0.6554818 
##         Silin 
##     0.0000000

5. Biomass monitored per year (monitoring effort)

10-most caught species characteristics

1. Catch-seasonality (10-most caught species)

2. Proportion of catches that are mature

Proportion of mature catches by year

Proportion of mature catches by gear

Sexual maturity

Maturity data were only gathered for a small subset of the total sampling effort. The observations which contain maturity data have the following characteristics:

## [1] "Total number of observations with maturity data: 0"
## [1] "Date range: Inf"  "Date range: -Inf"
## [1] "Number of maturity observations by species:"
## < table of extent 0 >